Unsupervised anomaly detection in Brain MRIs aims to identify abnormalities as outliers from a healthy training distribution. Reconstruction-based approaches that use generative models to learn to reconstruct healthy brain anatomy are commonly used for this task. Diffusion models are an emerging class of deep generative models that show great potential regarding reconstruction fidelity. However, they face challenges in preserving intensity characteristics in the reconstructed images, limiting their performance in anomaly detection. To address this challenge, we propose to condition the denoising mechanism of diffusion models with additional information about the image to reconstruct coming from a latent representation of the noise-free input image. This conditioning enables high-fidelity reconstruction of healthy brain structures while aligning local intensity characteristics of input-reconstruction pairs. We evaluate our method's reconstruction quality, domain adaptation features and finally segmentation performance on publicly available data sets with various pathologies. Using our proposed conditioning mechanism we can reduce the false-positive predictions and enable a more precise delineation of anomalies which significantly enhances the anomaly detection performance compared to established state-of-the-art approaches to unsupervised anomaly detection in brain MRI. Furthermore, our approach shows promise in domain adaptation across different MRI acquisitions and simulated contrasts, a crucial property of general anomaly detection methods.

翻译：脑部MRI无监督异常检测旨在从健康训练分布中识别异常值作为离群点。基于重构的方法通过生成模型学习重构健康脑部解剖结构，是该任务的常用技术。扩散模型作为新兴的深度生成模型类别，在重构保真度方面展现出巨大潜力，但在保留重构图像强度特征方面面临挑战，限制了其在异常检测中的性能。为解决此问题，我们提出利用包含噪声自由输入图像潜在表示的额外信息，对扩散模型的去噪机制进行条件约束。这种条件约束能够在保持输入-重构对局部强度特征一致性的同时，实现健康脑结构的高保真重构。我们通过公开可用的含多种病理数据集，评估了该方法的重构质量、域适应特性及最终分割性能。与脑部MRI无监督异常检测领域现有最先进方法相比，我们提出的条件约束机制能有效减少假阳性预测，实现更精确的异常区域分割，显著提升异常检测性能。此外，该方法在跨不同MRI采集协议和模拟对比度的域适应方面展现出潜力，这是通用异常检测方法的关键特性。